Abstract
Distributed automatic weather stations networks (AWSN) play an important role in modern weather forecasting and digitalization of agriculture. These networks allow to monitor various environment parameters and transmit actual data in real time to data centers, which makes possible to dramatically increase the efficiency of technical processes controlling farming. Since these networks may cover large areas, weather stations may use other stations to connect to gateways via multihop routes. The nodes are deployed outdoors and they are subject to failures due to harsh environment, deterioration of equipment, battery discharge, etc. If a station is used as a relay for other nodes, after its failure other stations may also become unavailable. To study the network reliability in this paper we propose a general methodology, consisting of six consolidated procedures, and apply the apparatus of the multidimensional alternating stochastic processes. We demonstrate the application of this analytical method for a special case of the minimal topology AWSN. General topology cases are studied with the use of the simulation approach. To study the numerical results, a discrete-event simulation model in Python language was developed. The paper presents numerical reliability analysis for three types of topologies: a simple network with three stations, a forest of ternary trees and random multihop networks with one or more gateways. In all scenarios we estimate reliability for the cases of static and dynamic routing. Different ways to enhance the distributed network reliability are discussed.
The reported study was funded by RFBR, projects number 19-29-06043 and 17-07-00142.
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Notes
- 1.
Experiment source code: https://github.com/larioandr/2019-dccn-sensors.
References
Suciu, G., Ijaz, H., Zatreanu, I., Drăgulinescu, A.-M.: Real time analysis of weather parameters and smart agriculture using IoT. In: Poulkov, V. (ed.) FABULOUS 2019. LNICST, vol. 283, pp. 181–194. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-23976-3_18
Moummadi, K., Abidar, R., Medromi, H.: Generic model based on constraint programming and multi-agent system for M2M services and agricultural decision support. In: 2011 International Conference on Multimedia Computing and Systems (ICMCS), pp. 1–6 (2011)
Lee, M., Hwang, J., Yoe, H.: Agricultural production system based on IoT. In: 16th IEEE International Conference on Computational Science and Engineering, Sydney, NSW, pp. 833–837 (2013)
Fridzon, M.B., Ermoshenk, Yu.M: Development of the specialized automatic meteorological observational network based on the cell phone towers and aimed to enhance feasibility and reliability of the dangerous weather phenomena forecasts. Russ. Meteorol. Hydrol. 34(2), 128–132 (2009)
Sarkar, I., Pal, B., Datta, A., Roy, S.: Wi-Fi-based portable weather station for monitoring temperature, relative humidity, pressure, precipitation, wind speed, and direction. In: Tuba, M., Akashe, S., Joshi, A. (eds.) Information and Communication Technology for Sustainable Development. AISC, vol. 933, pp. 399–404. Springer, Singapore (2020). https://doi.org/10.1007/978-981-13-7166-0_39
Ahmad, L., Habib Kanth, R., Parvaze, S., Sheraz Mahdi, S.: Automatic weather station. Experimental Agrometeorology: A Practical Manual, pp. 83–87. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-69185-5_12
Fang, Z., Zhao, Z., Du, L., Zhang, J., Pang, C., Geng, D.: A new portable micro weather station. In: 2010 IEEE 5th International Conference on Nano/Micro Engineered and Molecular Systems, Xiamen, pp. 379–382 (2010). https://doi.org/10.1109/NEMS.2010.5592239
Kodali, R.K., Mandal, S.: IoT based weather station. In: 2016 International Conference on Control, Instrumentation, Communication and Computational Technologies (ICCICCT), Kumaracoil, pp. 680–683 (2016). https://doi.org/10.1109/ICCICCT.2016.7988038
Snyder, R.L., Brown, P.W., Hubbard, K.G., Meyer, S.J.: A guide to automated weather station networks in North America. In: Stanhill, G. (ed.) Advances in Bioclimatology. Advances in Bioclimatology, vol. 4, pp. 1–61. Springer, Heidelberg (1996). https://doi.org/10.1007/978-3-642-61132-2_1
Aminev, D., Zhurkov, A., Polesskiy, S., Kulygin, V., Kozyrev, D.: Comparative analysis of reliability prediction models for a distributed radio direction finding telecommunication system. In: Vishnevskiy, V.M., Samouylov, K.E., Kozyrev, D.V. (eds.) DCCN 2016. CCIS, vol. 678, pp. 194–209. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-51917-3_18
Rykov, V.V., Kozyrev, D.V.: Reliability model for hierarchical systems: regenerative approach. Autom. Remote Control 71(7), 1325–1336 (2010). https://doi.org/10.1134/S0005117910070064
Rykov, V.V., Kozyrev, D.V.: Analysis of renewable reliability systems by markovization method. In: Rykov, V.V., Singpurwalla, N.D., Zubkov, A.M. (eds.) ACMPT 2017. LNCS, vol. 10684, pp. 210–220. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-71504-9_19
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Aminev, D., Golovinov, E., Kozyrev, D., Larionov, A., Sokolov, A. (2019). Reliability Evaluation of a Distributed Communication Network of Weather Stations. In: Vishnevskiy, V., Samouylov, K., Kozyrev, D. (eds) Distributed Computer and Communication Networks. DCCN 2019. Lecture Notes in Computer Science(), vol 11965. Springer, Cham. https://doi.org/10.1007/978-3-030-36614-8_45
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